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首页> 外文期刊>Journal of Scientific Computing >Edge Detection by Adaptive Splitting
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Edge Detection by Adaptive Splitting

机译：通过自适应分割进行边缘检测

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In this paper we propose an algorithm (EDAS-d) to approximate the jump discontinuity set of functions denned on subsets of R~d. We have limited our study to the ID (EDAS-1) and 2D (EDAS-2) versions of the algorithm. Theoretical and computational results prove its effectiveness in the case of piecewise continuous ID functions and piecewise constant 2D functions. The algorithm is based on adaptive splitting of the domain of the function guided by the value of an average integral. EDAS-d exhibits a number of attractive features: accurate determination of the jump points, fast processing, absence of oscillatory behavior, precise determination of the magnitude of the jumps, and ability to differentiate between real jumps (discontinuities) and steep gradients. Moreover, low-dimensional versions of EDAS-d can be used for solving higher dimensional problems. Computational experiments also show that EDAS-d can be applied to solve some problems involving general piecewise continuous functions. EDAS-1 and EDAS-2 have been used to determine edges in 2D-images. The results are quite satisfactory for practical purposes.
机译：在本文中，我们提出了一种算法（EDAS-d）来逼近定义在R〜d子集上的函数的跳跃间断集。我们将研究限于算法的ID（EDAS-1）和2D（EDAS-2）版本。理论和计算结果证明了其在分段连续ID函数和分段常数2D函数中的有效性。该算法基于以平均积分值指导的功能域的自适应分裂。 EDAS-d具有许多吸引人的功能：准确确定跳跃点，快速处理，没有振荡行为，精确确定跳跃幅度以及区分真实跳跃（不连续性）和陡峭梯度的能力。而且，EDAS-d的低维版本可用于解决高维问题。计算实验还表明，EDAS-d可用于解决一些涉及一般分段连续函数的问题。 EDAS-1和EDAS-2已用于确定2D图像中的边缘。对于实际目的，结果是令人满意的。

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